Exploring the Emotional Dimension of French Online Toxic Content
Valentina Dragos, Delphine Battistelli, Fatou Sow, Aline Etienne
Abstract
One of the biggest hurdles for the effective analysis of data collected on social platforms is the need for deeper insights on the content and meaning of this data. Emotion annotation can bring new perspectives on this issue and can enable the identification of content–specific features. This study aims at investigating the ways in which variation in online content can be explored through emotion annotation and corpus-based analysis. The paper describes the emotion annotation of three data sets in French composed of extremist, sexist and hateful messages respectively. To this end, first a fine-grained, corpus annotation scheme was used to annotate the data sets and then several empirical studies were carried out to characterize the content in the light of emotional categories. Results suggest that emotion annotations can provide new insights for online content analysis and stronger empirical background for automatic content detection.
BibTeX
@inproceedings{dragos-etal-2024-exploring,
title = "Exploring the Emotional Dimension of {F}rench Online Toxic Content",
author = "Dragos, Valentina and
Battistelli, Delphine and
Sow, Fatou and
Etienne, Aline",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.608/",
pages = "6945--6954"
}